The COVID-19 pandemic is the defining health crisis of the world in 2020 and the world economy is affected as well. Bangladesh is also one of the impacted countries, which needs to conduct sufficient tests to identify patients and accordingly adopt measures to limit the massive outbreak of this viral infection. But due to economic drawbacks and also unavailability of testing equipment, Bangladesh is lagging critically behind in test numbers. This study shows a pool testing method named Conditional Cluster Sampling (CCS) that utilizes soft computing and data analysis techniques to reduce the expense of total testing equipment. The proposed method also demonstrates its effectiveness compared to the traditional individual testing method. Firstly, according to patients' symptoms and severity of their conditions, they are classified into four classes- Minor, Moderate, Major, Critical. After that Random Forest Classifier (RFC) is used to predict the class. Then random sampling is done from each class according to CCS. Finally, using Monte Carlo Simulation (MCS) for 100 cycles, the effectiveness of CCS is demonstrated for different probability levels of infection. It is shown that the CCS method can save up to 22% of the test kits that can save a huge amount of money as well as testing time.This article analyses how border guards as members of a state organisation shape the movement of non-nationals into the territory of a nation state. Based on ethnographic fieldwork on the Swiss Border Guard (SBG), it explores the rationalities-understood as stabilised ways of reasoning and acting-that characterise practices within this state organisation. Combining organisational and structuration theory with a street-level bureaucracy perspective allows for a differentiated analysis of the various facets of border guards' everyday work. Four rationalities of border-control practices are identified and compared security, humanitarian, cost-calculation, and pragmatic rationality. I argue that, by considering both the specific goals and imperatives of border control and the characteristics of street-level bureaucrats acting within a state organisation, these entangled logics explain the complex and incoherent social reality of border control. https://www.selleckchem.com/products/pf-06826647.html More generally, the results contribute to organisational theory by pointing to the importance of taking into account that multiple entangled rationalities structure the practices of an organisation's members.A total number of 3080 SARS-CoV-2 genomes from all continents are considered from the NCBI database. Every accessory protein ORF6, ORF7b, and ORF10 of SARS-CoV-2 possess a single missense mutation in less than 1.5% of the 3080 genomes. It has now been observed that different non-synonymous mutations occurred in these three accessory proteins. Most of these rare mutations are changing the amino acids such as hydrophilic to hydrophobic, acidic or basic to hydrophobic, and vice versa etc. So these highly conserved proteins might play an essential role in virus pathogenicity. This study opens a question whether it carries some messages about the virus rapid replications, and virulence.Invasive pulmonary aspergillosis, known as a complication in patients with severe respiratory syndromes, recently showed a correlation with COVID-19 pneumonia, and the clinical characteristics of COVID-19 associated pulmonary aspergillosis (CAPA) have been described. Unfortunately, infections by the Aspergillus genus are often diagnosed in post-mortem time, because of diagnostic delays and a rapid worsening of respiratory conditions. Literature data document, in fact, only few cases of COVID-19 Aspergillus niger coinfection. The aim of this study was to describe a case of a VAP-related probable pulmonary aspergillosis by Aspergillus niger in a COVID-19 patient. Despite the definition of fungal etiology and the rapid administration of antifungal therapy, the patient died while on ventilator support because of severe respiratory impairment.COVID-19 infection is a new disease mainly affecting the respiratory system but is also accompanied by many extra-pulmonary manifestations. A case of a 47-year old male with unique myocardial fibrosis after COVID-19 infection involving the left ventricular wall, intraventricular septum and almost complete damage of interatrial septum, in combination with asymptomatic severe sinus arrest episodes related to mild obstructive sleep apnea syndrome is described here.The pandemic of SARS-Coronavirus-2 (Coronavirus-19) has been progressing by the increasing trend of the cases as well as deaths with neither vaccine nor drug is rationally used to stop the viral spread over. This study aims to perform an integrated virtual screening of compounds that had been identified from Carica papaya leaves, which are proposed to be a herbal treatment for SARS-Coronavirus-2. The screening was initiated by evaluating the 40 compounds from Carica papaya leaves for their drug-like likeness property. The selected compounds were then secondly screened using carcinogenic and toxicity filters. Further selected compounds were thirdly screened for their pharmacokinetic profile and the screening was lastly performed by docking the third selected compounds against multiple protein targets of SARS-Coronavirus-2 employing 3-chymotrypsin-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent-RNA-polymerase (RdRp), endonuclease (EndoU), S1 and S2 region of spike protein. The results show that 20 of 40 compounds, which meet the requirements of drug-like likeness, carcinogenicity-toxicity filter, and pharmacokinetic profiles, can interact with the multiple protein targets of SARS-Coronavirus-2 with the order from high to low affinity as follows S1 &gt; 3CLpro &gt; EndoU &gt; RdRp &gt; PLpro &gt; S2. In conclusion, Carica papaya leaves are worth to be proposed for further in vitro study against SARS-Coronavirus-2 at both molecular and cellular levels.The recent outbreak of the COVID-19 affected millions of people worldwide, yet the rate of infected people is increasing. In order to cope with the global pandemic situation and prevent the spread of the virus, various unprecedented precaution measures are adopted by different countries. One of the crucial practices to prevent the spread of viral infection is social distancing. This paper intends to present a social distance framework based on deep learning architecture as a precautionary step that helps to maintain, monitor, manage, and reduce the physical interaction between individuals in a real-time top view environment. We used Faster-RCNN for human detection in the images. As the human's appearance significantly varies in a top perspective; therefore, the architecture is trained on the top view human data set. Moreover, taking advantage of transfer learning, a new trained layer is fused with a pre-trained architecture. After detection, the pair-wise distance between peoples is estimated in an image using Euclidean distance.